Invention Grant
US08544087B1 Methods of unsupervised anomaly detection using a geometric framework
有权
使用几何框架进行无监督异常检测的方法
- Patent Title: Methods of unsupervised anomaly detection using a geometric framework
- Patent Title (中): 使用几何框架进行无监督异常检测的方法
-
Application No.: US12022425Application Date: 2008-01-30
-
Publication No.: US08544087B1Publication Date: 2013-09-24
- Inventor: Eleazar Eskin , Andrew Oliver Arnold , Michael Prerau , Leonid Portnoy , Salvatore J. Stolfo
- Applicant: Eleazar Eskin , Andrew Oliver Arnold , Michael Prerau , Leonid Portnoy , Salvatore J. Stolfo
- Applicant Address: US NY New York
- Assignee: The Trustess of Columbia University in the City of New York
- Current Assignee: The Trustess of Columbia University in the City of New York
- Current Assignee Address: US NY New York
- Agency: Baker Botts, L.L.P.
- Main IPC: G06F12/14
- IPC: G06F12/14 ; G06F12/16
Abstract:
A method for unsupervised anomaly detection, which are algorithms that are designed to process unlabeled data. Data elements are mapped to a feature space which is typically a vector space . Anomalies are detected by determining which points lies in sparse regions of the feature space. Two feature maps are used for mapping data elements to a feature apace. A first map is a data-dependent normalization feature map which we apply to network connections. A second feature map is a spectrum kernel which we apply to system call traces.
Information query
